nificant reductions in anxiety and stress levels during the COVID-19 pandemic and could be used as a population-level mental health intervention during natural disasters and other emergencies.

RR2-10.2196/19292.

RR2-10.2196/19292.

Nursing home residents are at high risk of complications and death due to COVID-19. Lack of resources, both human and material, amplifies the likelihood of contamination in these facilities where a single employee can contaminate dozens of residents and colleagues. Improving the dissemination of and adhesion to infection prevention and control (IPC) guidelines is therefore essential. Serious games have been shown to be effective in developing knowledge and in increasing engagement, and could motivate nursing home employees to change their IPC practices.

Our aim is to assess the impact of “Escape COVID-19,” a serious game designed to enhance knowledge and application of IPC procedures, on the intention of nursing home employees to change their IPC practices.

We will carry out a web-based randomized controlled trial following the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines and incorporating relevant elements of e exact changes considered by the participants. Factors associated with participant willingness or reluctance to change behavior will also be assessed. Attrition will also be assessed at each stage of the study.

The study protocol has been presented to our regional ethics committee (Req-2020-01262), which issued a declaration of no objection as such projects do not fall within the scope of the Swiss federal law on human research. Data collection began on November 5, 2020, and should be completed by December 4, 2020.

This study should determine whether “Escape COVID-19,” a serious game designed to improve compliance with COVID-19 safe practices, modifies the intention to follow IPC guidelines among nursing home employees.

DERR1-10.2196/25595.

DERR1-10.2196/25595.For the target-tracking problem, full state of the target may not be available since it may be expensive or impossible to obtain. Thus, the state needs to be reconstructed or estimated only according to measured inputs and outputs. The impossible case that all followers can measure the target directly yields the study of distributed methods, thus reducing the communication and computation resource while resulting in more robustness. This article confronts these problems by addressing a distributed iterative finite impulse response (DIFIR) consensus filter for leader-following systems. A solution to the underlying problem is obtained by involving a distributed measurement model wherein not only the neighbors’ estimates are applied but also the directed measurement data are used, and expressed by a computationally efficient iterative algorithm. Applying this DIFIR strategy, it is shown that the leader’s estimates by all followers reach H∞ consensus, whose value is the local unbiased estimates of the leader. Then, the result is extended to multiagent systems whose leader has unknown inputs. Incorporating the input estimates, a new DIFIR is proposed. Finally, examples are given to illustrate the consistency and robustness of the developed new design techniques.A key energy consumption in steel metallurgy comes from an iron ore sintering process. selleck Enhancing carbon utilization in this process is important for green manufacturing and energy saving and its prerequisite is a time-series prediction of carbon efficiency. The existing carbon efficiency models usually have a complex structure, leading to a time-consuming training process. In addition, a complete retraining process will be encountered if the models are inaccurate or data change. Analyzing the complex characteristics of the sintering process, we develop an original prediction framework, that is, a weighted kernel-based fuzzy C-means (WKFCM)-based broad learning model (BLM), to achieve fast and effective carbon efficiency modeling. First, sintering parameters affecting carbon efficiency are determined, following the sintering process mechanism. Next, WKFCM clustering is first presented for the identification of multiple operating conditions to better reflect the system dynamics of this process. Then, the BLM is built under each operating condition. Finally, a nearest neighbor criterion is used to determine which BLM is invoked for the time-series prediction of carbon efficiency. Experimental results using actual run data exhibit that, compared with other prediction models, the developed model can more accurately and efficiently achieve the time-series prediction of carbon efficiency. Furthermore, the developed model can also be used for the efficient and effective modeling of other industrial processes due to its flexible structure.The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.In terms of pipeline leak detection, the unavoidable fact is that existing data could not provide enough effective leak data to train a high accuracy model. To address this issue, this article proposes mixed generative adversarial networks (mixed-GANs) as a practical way to provide additional data, ensuring data reliability. First, multitype generative networks with heterogeneous parameter-updating mechanisms are designed to explore a variety of different solutions and eliminate the potential risks of instable training and scenario collapse. Then, based on expert experience, two data constraints are proposed to describe leak characteristics and further evaluate the quality of generated leak data in the training process. Through integrating the particle swarm optimization algorithm into generative model training, mixed-GAN has better generation performance than the conventional gradient descent algorithm. Based on the above-mentioned contents, the proposed model is able to provide satisfactory leak data with different scenarios, contributing to data quantity expansion, data credibility enhancement, and data variety enrichment.